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scheduling of the distributed energy is organized based on the existing data ... INTEGRATION OF AQUIFER THERMAL ENERGY SYSTEMS (ATES). INTO VIRTUAL POWER .... by optimizing the operation of HVAC system with. ATES. Two heat ...

Fifth German-Austrian IBPSA Conference RWTH Aachen University

INTEGRATION OF AQUIFER THERMAL ENERGY SYSTEMS (ATES) INTO VIRTUAL POWER PLANT AS A SOURCE OF FLEXIBILITY B. Bozkaya, W. Zeiler, G. Boxem University of Technology Eindhoven, Department of the Built Environment Den Dolech 2, Eindhoven Netherlands

ABSTRACT Rapid deployment of distributed energy resources (DER) has changed traditional billing system into dynamic pricing due to the fluctuations in power generation. Virtual power plant (VPP) concept has emerged to take full economic benefit of electricity market and enhancing reliability of electricity system by aggregating the capacity of storage and controllable technologies. It also add flexibility into energy use. Imbalances between demand and supply due to the prediction error in electricity market is inevitable and remained as a problem for end user in VPP concept. On the one hand, there is a penalty for imbalances, on the other hand, there is additional operational cost for the compensation. In this study, the future prospects of aquifer thermal energy storage (ATES) has been investigated under the control of multiagent system (MAS). Restrictions and capabilities of ATES have been introduced correspondingly.

1.INTRODUCTION Today’s traditional power supply system is mainly organized in top down manner since centralized energy generation systems (large power plants etc.) are mainly responsible for power generation. Prescheduling of the distributed energy is organized based on the existing data (Zhang, Tezuka, Ishihara, & Mclellan, 2012). However, renewable energy sources (RES) have been increasingly added to traditional power grid. Rapid deployment of DER have increased the complexity in future power grid since their power generation is highly unpredictable and intermittent. On the demand side, electrical devices (electric vehicle, heat pump etc.) are rapidly penetrating into power grid and their operation is controllable and are expected to compensate power fluctuations. Therefore, It has become necessary to develop smart control systems act under real time dynamics to match the load with generation; thereby, avoiding the waste energy. Growing environmental concerns and energy shortage have necessitated the optimum integration of RES into power grid (Zhang et al., 2012) (Jun et al., 2011). Smart grid technologies have allowed us to control the energy balance between demand and supply by integrating

RES with controllable devices on the demand side (Kok et al., 2009). In this respect, some smart grid concepts (e.g. microgrid, nano-grid and VPP) have been introduced. They actively participate in power grid to manage the energy flow within building, between buildings and built environment. Participants of energy management within these concepts mainly consist of DER (e.g. RES ), energy storage devices (e.g. batteries and water tank) and controllable loads (e.g. heat pumps, electric vehicle) (Jun et al., 2011). VPP participates in electricity market and trade electricity depending on bilateral contract between consumer, prosumer and power plants. The owner of VPP (also called aggregator) is responsible for power supply within VPP. Aggregator can achieve more stable and reliable power supply by adding flexibility at high level of power by simultaneously employing nondispatchable, dispatchable sources and storage devices; thus the owner of RES can get higher economic value for his unreliable energy generation by joining VPP (Tascikaraoglu et al., 2014). The primary aim of aggregator is to determine optimal schedule with minimal cost and maximum profit by negotiating with regulator of electricity market; thus, It is significant for aggregator to predict both demand and generation side in VPP to develop reliable market bidding strategy. (Handschin et al., 2006). At that point, there is a risk of not meeting the needed power agreed on long or mid-term contracts due to the uncertainties on both generation and demand side. Aggregator can compensate the imbalances by employing controllable generators (e.g. Combined Heat and Power Generator (CHP)) and storage devices (Pandzic et al. 2013). However, Imbalances between predicted and actual energy result in profit losses (Tascikaraoglu et al., 2014) (Pandzic, Morales, et al. 2013). The integration of large scale renewable resources has led to switch the organization of power supply from top-down to down-top manner in order to gain full economic benefit of distributed energy resources (Hommelberg et al., 2007). As part of this effort, there has been an opportunity to optimize the energy flow in residential and commercial buildings by treating each building as an active participant under

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building energy management systems (BEMSs), thus can be offered to VPP (Kok et al., 2009) (Palizban et al., 2014).

Investigated publications have been used to determine the following issues: - Capabilities and restrictions of ATES in VPP - The future role of ATES in balancing market - The effectiveness of MAS in controlling VPP in combination with BEMS


Figure1: Future of the electricity system (BayodRujula, 2009)

A significant amount of researches have been conducted to investigate the role of different electrical devices and storage systems in VPP market (Zapata et al., 2014), (Moghaddam et al., 2013), (Mohammadi., 2011), (Houwing et al., 2009). As business case, on the one hand, there is a risk to pay penalty, on the other hand, there is an additional operational costs to compensate it. Therefore, different devices in the system may offer various potential and capabilities in taking full benefit of VPP market. ATES can deal with the problem by providing flexibility in the system with its storage capacity. Kranz and Frick, (2013) proposed ATES as system with large heat storage capacity that operate seasonal mode. ATES can offer vast amount of flexibility with their high heat storage capacity and seasonal operation. However, there are some challenges associated with integrating ATES into electricity market. Therefore, in this paper, the future role of ATES in VPP is investigated. In addition, the effectiveness of MAS in BEMS for demand side management in combination with VPP will be introduced.

Flexibility in the energy use is provided under demand side management and demand response concepts. Therefore, it is necessary to implement BEMSs that perform under real time dynamics to adjust energy use; thereby, integrate building into balancing market in VPP. Even though, several stochastic models have been introduced to better predict energy demand and generation so far, it is not possible to completely avoid imbalances as a result of uncertainties in the system (Jain, Smith et al., 2014), (Virote and Neves-Silva, 2012) (Soman et al., 2010). Charging and discharging capabilities of recently added devices assist to deal with fluctuations from energy generation from renewable sources (Zhang et al., 2012). The extent of flexibility vary depending on the number and characteristics of elements (shiftable loads, on-site generation etc.) in the system. Flexibility in the system help user to adjust the demand profile as it is required from the electricity market (Gelazanskas and Gamage, 2014). In a VPP, demand side management (DSM) is expected to play key role in dealing with balancing problem. In case of not meeting agreed demand in bilateral contract which is also called Non-Supplied Demand, VPP owner can employ storage devices. In case, reserves are not enough to supply demand, consumer may decrease the consumption at specific period of time by postponing or turning down the operation of devices with respect to comfort range. On the other hand, it is possible to compensate power by employing controllable generators (e.g. CHP and biomass). From technical perspective, network must be capable of transmitting the amount of power at the given specific period of time (Faria et al., 2014).

The paper is organized as follows. Section 2 explains flexibility concept in comply with VPP, Section 3 proposes multi-agent system architecture, simulation results will be presented in Section 4. Conclusion will be given following Section 4.


Figure2: Demand side management(‘Load’)

Scientific websites such as scienceDirect and IEEE were used to investigate publications related MAS implementation in BEMS, imbalances, flexibility and the role of different storage system in VPP.

ESS (energy storage system) is used for load leveling and power smoothing issues. Load leveling is also can be described as shifting energy utilization from

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on-peak periods to non-peak periods. Specifically, thermal energy storage has a powerful effect on shaping the demand, since their operation are controllable and responsible for the majority of demand (Arteconi et al., 2013). Yau et al. (2012) reviewed that 3MWe of electricity demand has been removed from on-peak hours using chilled water storage technology. It plays vital role in decreasing the cost of energy. ESS may discharge/charge energy in seasonal, monthly, weekly and daily cycle. As the capacity of energy storage increases, the amount of demand that can be met increases. As business case, Instead of changing infrastructure and the capacity of generator, larger storage system can help the system provide the necessary amount of energy (KoohiKamali et al., 2013). There are several different factors affecting the suitability of storage system for the target case. These factors can be cost, energy density, response time, efficiency, storage capacity, technological maturity and so on (Yekini Suberu et al., 2014). As It can be seen from Figure3, some storage devices may offer lower discharge time with low storage capacity, whereas, others may offer high capacity with longer discharge time.

Figure3: Qualifications of various storage types (Mass storage of energy panorama, 2013) Yekini Suberu et al. (2014) also concluded that there is no ideal ESS to compensate fluctuations in power. Intended result can be determined by combining these factors. Storage system with high density, small response time and high capacity has advantages over other ESS to control fluctuations. However, As long as electrical characteristics of ESS do not comply with restrictions imposed by other factors (electricity tariff, climate profile, cost etc.), It is no longer a feasible solution for the target case (Yau & Rismanchi, 2012). Steffen (2012) evaluated the effectiveness of pumped-hydro storage on dealing with fluctuations. It has been determined that there are some constraints depending on the lack of suitable locations. There is also another restrictions on the size of reservoir;

therefore, It is not capable of storing excess energy from RES for long period of time. Another promising technology, compressed air storage (CAES) which has large scale energy storage has been evaluated in several studies. It has been concluded in Dahraie et al. (2012) that CAES is good at storing wind fluctuations in large scale, however, pose some challenges in terms of limitations in size. On the other hand, it is concluded in Taylor & Halnes (2010) that batteries are not applicable for large scale energy storage due to the high cost. It can be implied that energy can be stored in large scale in built environment using pumped storage technologies which might be more feasible compared to battery technology. ATES with high storage capacity is expected provide high amount of flexibility as it operates in seasonal mode. The capacity of ATES is not limited to a certain amount of energy since it is not restricted with geometrical boundaries; whereas, other previously mentioned storage systems have some limitations (Kranz & Frick, 2013). However, ATES is highly depends on the hydrological conditions of underground (Kousksou, Bruel, Jamil, El Rhafiki, & Zeraouli, 2014). There is also balancing issues concerning discharged and charged heat to the ground, since there is always imbalances in cold and heat demand in the buildings. It is compensated with additional operations of components within the system. These components can be cooling tower, air handling unit for cooling the water and solar thermal, heat pump for heating up the water. The operation of these devices varies depending on the certain environmental conditions and market prices. Overall performance can be increased with the contribution of additional devices within the system (Kranz & Frick, 2013). Efficiency of charged heat to and discharged heat from the ground can be expressed as follows:  =




(1) (2)

Recent studies has shown that BEM can assist to operate system in line with certain design and operating parameters; thereby, increase the COP of ATES. Kranz and Frick, (2013) achieved an increase in COP for cooling from 3.6 to 7.8 over the period of time and concluded that COP can be even increased over 18. Miyata et al.,(2007) has achieved 30% energy saving and increased COP from 3.02 to 5.04 by optimizing the operation of HVAC system with ATES. Two heat pump coupled ATES system in thermal balance in the Klina hospital in Belgium which leads to COP 5.9 for heating, 26.1 for cooling (Vanhoudt et al., 2011).

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With this motivation in the mind, in VPP concept, it is possible to operate additional units during nonpeak hours for balancing issues and consuming less energy during peak hours when the prices are high. In the light of these information, it can be implied that flexibility can be offered by taking into account long term energy optimization of ATES. Under proper control strategies, It is possible to take full economic benefit of electricity market in the long run.

3.1.IMBALANCE REDUCTION Solution for imbalances between demand and supply varies depending on the capability of the infrastructure and components within building or VPP. On the one hand, there is a penalty for imbalance and on the other hand, there is operational costs for the compensation of imbalance. At that point, aggregator is responsible for getting the highest profit for his commitment into VPP. Several optimization methods have been introduced to deal with complexity of imbalance in order to take full economic benefit of DG. An optimization algorithm was developed using a mixed integer linear programming model (MILP) to implement VPP that consists of several micro-CHP unit and a PV installation in (Zapata et al., 2014). It has been determined that rescheduling the operation of CHP assist to reduce imbalances by %90. (Moghaddam et al., 2013) proposed a scheduling model by combining wind power with hydro plant’s capacity to reduce imbalances. The results indicated that hydro plants were capable of decreasing the imbalances costs. (Houwing et al., 2009) has combined wind power with micro-CHP in his system, and determined that micro-CHP assist to reduce the amount of imbalance by %73 and imbalance cost by %38. Warmer et al. determined that agent based electric market can be a promising solution to deal with fluctuations using PowerMatcher software and also concluded that large storage capacity can assist to deal with high portion fluctuations.

Figure4: Compensation problem of deficient energy As it is described in Figure4, when deficient energy occurs in the system, it is not possible to fulfill it quickly within ATES concept. In thermal energy storage concept, it is possible to store power as heat

in small storage tank in desirable degrees (high quality heat) and can be used for short term storage purposes since it can fulfill the demand without employing additional devices. However, conventional ATES systems with high storage capacity are operating around 8-12℃ in cold well, 15-18℃ in hot well; therefore, It is necessary to employ additional devices (heat pump, electric motor etc.) to further increase or decrease the temperature. Thus, it is not possible to turn off the system and waiting for heat storage fulfill the demand. At that point, there is a restriction associated with flexibility that can be translated into financial benefit for the end user. It is the conflict between benefit from the compensation and cost for the compensation.

4. INTEGRATION OF ATES ATES is widely used in Netherlands. Majority of ATES are used in office buildings due to the fact that it is used for seasonal storage and needs large capacity where it can charge/discharge high amount of heat from environment (Kousksou et al., 2014). Groundwater is used to transfer thermal energy in and out of an aquifer in ATES systems. Low quality heat or cold such as solar heat or waste heat is stored and utilized in the system. Excess energy is also considered as low quality energy. ATES systems can store high amount of low quality heat with its high capacity during off-peak time and later, water is withdrawn for space heating and cooling during peak times. Therefore, it would be wise to optimize ATES operation; thereby, taking advantage of its large capacity in smart grid concept. On the other hand, majority of ATES systems in Netherlands are not working efficiently due to the fact that aquifer systems are designed based on rule of thumb method. It is expected that electricity market will push end user to optimize ATES operation to take economic benefit within smart grid concept. As It is discussed previously, There are long term and short term restrictions on integration of ATES into electricity market. In the long term, It is possible to store excess energy from distributed energy sources such as solar thermal and also it is possible to operate additional devices such as cooling tower, air handling unit to store energy under the ground. Therefore, in the seasonal time period, ATES may provide flexibility and opportunities for taking advantage of electricity market. However, in the short period, since the stored energy in ATES is low quality thermal energy, it is necessary to employ additional devices to make it higher quality thermal energy that can be utilized by the end user. On the other hand, most of the time, buildings have certain thermal demand pattern, either heating or cooling is more than another. Therefore, For many projects, thermal imbalances in wells gradually increases each year and it leads to future interferences between wells. Dominating well

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Fifth German-Austrian IBPSA Conference RWTH Aachen University

eventually undermines the operation of other well and make the system useless after years due to the fact that system will no longer operate in line with design parameters, which eventually, decrease the efficiency and reliability of the system. -In the long term, ATES operation is expected to be in thermal balance to prevent future mutual interference between wells. -In the short term, ATES has an integration problem into VPP in terms of compensation imbalances in the system. Integration of ATES into power grid necessitates the optimization of its operation under these restrictions. Optimization of ATES behavior necessities controlling the behavior of each components under BEMS in combination with VPP. However, today’s traditional BEMSs lack intelligence to control such complex system where numerous dynamics play role (Klein et al. 2012). MAS is one of the best available concept for modeling energy management system where each components in the system described as autonomous agents. In this way, artificial intelligence is distributed instead of centralizing the control. Agents forming MAS provide holistic solutions by intelligently coordinating each other (Logenthiran et al., 2011). Therefore, MAS will be used to model each entity in the system as an autonomous agent. Solution for the compensation of imbalances in electricity market can be dealt with extra cold and warm well (see in Figure5). Waste power or heat can be sunk into wells where it further decrease temperature in cold well and increase temperature in warm well. In this way, COP is increased and energy consumption is decreased. If there are still imbalances in the system, the operation of HVAC units can be postponed or turned down.

Figure5: Designated Multi-Agent System for ATES integration into VPP

Designated extra warm and extra cold well will be used for balancing issues to both compensate imbalance in electricity market and imbalances in warm and cold well. Local power management will be responsible for the participation in electricity market depending on the negotiation with LVPP and managing local renewable energy sources within the building. Personal agent will be used to learn from human behavior to optimize the energy use and predict the future behavior of the components (Yang & Wang, 2013). Following result will be determined:

Figure6: MAS architecture in VPP - Effectiveness of proposed MAS system on integrating ATES into balancing market while ensuring the balance between injected heat and cold to the ground in order to prepare ATES for the next season. Designated MAS architecture in building level can be integrated into Community, Regional and System scale VPP respectively where artificial intelligence is distributed on each layer forming whole system (see in Figure6). In this way, information is provided from bottom to top, each layer makes information available for the upper layer (Dimeas & Hatziargyriou, 2007). There is decreasing trend in the amount of information as information exchanged from bottom to top. Major part of data is handled in building level which is called individual layer. All the components responsible for demand and supply profile in the system is dealt in this layer. Described models are embedded in relevant agent and links are established correspondingly; thereby, forming whole system. MAS in the individual layer communicate with other MASs in community layer to transfer energy depending on the decision from regional layer(VPP). Thus, flexibility within a building can be offered external parties to enable them operate effectively. Regional layer is considered as RVPP where several community layers (LVPPs) are connected. Each regional layer introduces its own energy trend and mainly responsible for market participation by taking

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Fifth German-Austrian IBPSA Conference RWTH Aachen University

advantage of the capacity of LVPPs. Several regional layers form the top level which is defined as system layer (Dimeas & Hatziargyriou, 2007).

been adjusted in line with comfort level to further decrease the energy consumption. However, system capabilities is not being considered under VPP strategies.

4.1.CASE STUDY The mono-well used at Kropman is installed by the company GeoComfort and is of the type GT-15. As visible in the ground profile (Figure7), the first aquifer (used for the warm well) is located from 5 to 35 meters below the surface, the separating clay layer is located from 35 to 37.5 meters and the second aquifer (used for the cold well) is located from 37.5 to 58.5 meters below the surface. System is monitored using InsiteView developed by Priva. In the near future, two more wells will be incorporated into ATES system where it will be possible to apply designed MAS diagram in real case. Technically, in the system, the groundwater is never pumped up since the heat is exchanged in the upper layer of the ground. It helps owner avoid permit application procedures and some regulation. As It can be seen from figure, there are measurement devices placed strategically for controlling ATES with HVAC as well as the temperature set points in the building (see in figure7). It is office building and ATES is operating during the work hours. In the first step, modular simulation tool will be developed and applied to the case study in order to validate the model. Afterwards, designated MAS will be applied and necessary control strategies will be developed in relation to VPP concept.

Table1: Cumulative energy injected to the ATES in target case (Hoving J.H.K, 2014) In the future, this case will be analyzed and capabilities of ATES system in electricity market within VPP will be determined in line with proposed MAS. Behavior of the components in the system will be represented by agents forming multi-agent system based on concept; PowerMatcher. System will be monitored and recorded using InsiteView. The effectiveness of PowerMatcher in matching demand and supply has been proven in several studies (Hommelberg, M. P. F., 2007), (Kok et al., 2008). Approximately the water temperature in warm well is around 14℃ , whereas cold water temperature is around 6℃ in target case. COP changes depending on the temperature differences (3), (4) where it can be implied that if the differences decrease, higher COP can be achieved. For the target case, storing cold water lower than 6℃ and hot water higher than 14℃ would provide additional decrease in the energy consumption depending on the technical specification of heat pump. In the ATES system in Kropman, heating and cooling is provided with both heat pump and directly through heat exchanger depending on demand. Thus, the change in temperature will decrease the energy consumption in relation to temperature difference if it is used directly or in relation to COP if it is used through heat pump.

Figure7: Mono well ATES in InsiteView Currently, there is an optimization method being implemented to increase the efficiency of the system. In the control strategy, air handling unit has been employed to regenerate warm well in order to compensate imbalances between injected heat and cold into the ground (see in Table1). It has been used when outside temperature is cold enough during the night time. In addition, temperature set points have

Figure8: Proposed effect on imbalances In the light of these information it is possible to decrease the energy consumption by further increasing and decreasing the temperatures in two wells.

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Fifth German-Austrian IBPSA Conference RWTH Aachen University

5.CONCLUSION Integration of RES into power grid has led a change in organization of traditional grid system from updown to down-up manner. From business perspective, stochastic nature of renewable sources has switched traditional billing system to dynamic market pricing which necessitated the optimum integration of buildings in power grid under smart control of energy in order to take full economic benefit from electricity market. Inevitable imbalances between demand and generation remained as a problem for the participants of market. At that point, flexibility in the system help the system compensate the fluctuations by providing end user with opportunities to achieve more stable and reliable power. Capabilities of system for avoiding imbalances vary depending on the system components as it is reviewed in this paper. However, the future role of ATES is still not clear since it is used to store low quality thermal energy and need to operate additional devices to make it usable. Economic incentives provided under VPP market is expected to encourage the owner to optimize operation of ATES and search for the possibilities for the integration to the electricity market. Therefore, In this paper, it has been proposed that capabilities and restrictions of ATES within VPP concept will be analyzed as future work. On the one hand, there is an assigned role for the compensation of the imbalances in market, on the other hand, there is long term optimization imposed by imbalances between injected and discharged heat. Thus, optimum integration of ATES systems into electricity market is complex issue where numerous components and dynamics take place.

REFERENCES Zhang, Q., Tezuka, T., Ishihara, K. N., & Mclellan, B. C. (2012). Integration of PV power into future low-carbon smart electricity systems with EV and HP in Kansai Area, Japan. Renewable Energy,44,99– 108. Jun, Z., Junfeng, L., Jie, W., & Ngan, H. W. (2011). A multi-agent solution to energy management in hybrid renewable energy generation system. Renewable Energy, 36(5), 1352–1363. Kok, K., et al, (2009), 20th International Conference on Electricity Distribution: Smart houses for a smart grid, Page(s): 1 -12 Klein, L., Kwak, J. Y., Kavulya, G., Jazizadeh, F., Becerik-Gerber, B., Varakantham, P., & Tambe, M. (2012). Coordinating occupant behavior for building energy and comfort management using multi-agent systems. In Automation in Construction (Vol. 22, pp. 525–536).

Hadj-said, Y. ; Ploix, S. ; Galmiche, S. ; Bergeon, S. ;Brunotte, X, (2013), Canopea, an energy-smart home integrable into a smart-grid, page(s); 1-7 Logenthiran, T., Srinivasan, D., & Khambadkone, A. M. (2011). Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system. Electric Power Systems Research, 81, 138– 148. Palizban, O., Kauhaniemi, K., & Guerrero, J. M. (2014). Microgrids in active network management— Part I: Hierarchical control, energy storage, virtual power plants, and market participation. Renewable and Sustainable Energy Reviews, 1, 1–13. Yang, R., & Wang, L. (2013). Multi-zone building energy management using intelligent control and optimization. Sustainable Cities and Society, 6, 16– 21. Handschin, E., Neise, F., Neumann, H., & Schultz, R. (2006). Optimal operation of dispersed generation under uncertainty using mathematical programming. International Journal of Electrical Power & Energy Systems. Pandzic H., Kuzle, I., & Capuder, T. (2013). Virtual power plant mid-term dispatch optimization. Applied Energy, 101, 134–141. Pandzic, H., Morales, J. M., Conejo, A. J., & Kuzle, I. (2013). Offering model for a virtual power plant based on stochastic programming. Applied Energy, 105, 282–292. Hommelberg, M. P. F., Warmer, C. J., Kamphuis, I. G., Kok, J. K., & Schaeffer, G. J. (2007). Distributed control concepts using multi-agent technology and automatic markets: An indispensable feature of smart power grids. In 2007 IEEE Power Engineering Society General Meeting, PES. Dimeas, A. L., & Hatziargyriou, N. D. (2007). Agent based control of virtual power plants. Internation Conference on Intelligent Systems Applications, 1–6. El Bakari, K., & Kling, W. L. (2012). Fitting distributed generation in future power markets through virtual power plants. In 9th International Conference on the European Energy Market, EEM 12. Moghaddam, I. G., Nick, M., Fallahi, F., Sanei, M., & Mortazavi, S. (2013). Risk-averse profit-based optimal operation strategy of a combined wind farmcascade hydro system in an electricity market. Renewable Energy, 55, 252–259.

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Zapata, J., Vandewalle, J., & D’haeseleer, W. (2014). A comparative study of imbalance reduction strategies for virtual power plant operation. Applied Thermal Engineering. Houwing, M., Papaefthymiou, G., Heijnen, P. W., & Ilic, M. D. (2009). Balancing wind power with virtual power plants of micro-CHPs. In 2009 IEEE Bucharest PowerTech: Innovative Ideas Toward the Electrical Grid of the Future. Virote, J., & Neves-Silva, R. (2012). Stochastic models for building energy prediction based on occupant behavior assessment. Energy and Buildings, 53, 183–193. Jain, R. K., Smith, K. M., Culligan, P. J., & Taylor, J. E. (2014). Forecasting energy consumption of multifamily residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy, 123, 168–178. Soman, S. S., Zareipour, H., Malik, O., & Mandal, P. (2010). A review of wind power and wind speed forecasting methods with different time horizons. In North American Power Symposium 2010, NAPS 2010. Faria, P., Soares, T., Vale, Z., & Morais, H. (2014). Distributed generation and demand response dispatch for a virtual power player energy and reserve provision. Renewable Energy, 66, 686–695. Yekini Suberu, M., Wazir Mustafa, M., & Bashir, N. (2014). Energy storage systems for renewable energy power sector integration and mitigation of intermittency. Renewable and Sustainable Energy Reviews, 35, 499–514. Koohi-Kamali, S., Tyagi, V. V., Rahim, N. A., Panwar, N. L., & Mokhlis, H. (2013). Emergence of energy storage technologies as the solution for reliable operation of smart power systems: A review. Renewable and Sustainable Energy Reviews.

Operation for HVAC system with underground thermal storage system.


Taylor, J., & Halnes, A. (2010). Analysis of compressed air energy storage. In PCIC Europe Conference Record (pp. 1–5Gelazanskas, L., & Gamage, K. A. A. (2014). Demand side management in smart grid: A review and proposals for future direction. Sustainable Cities and Society, 11,22–30. Kok, K., Derzsi, Z., Gordijn, J., Hommelberg, M., Warmer, C., Kamphuis, R., & Akkermans, H. (2008). Agent-based electricity balancing with distributed energy resources, a multiperspective case study. In Proceedings of the Annual Hawaii International Conference on System Science Yau, Y. H., & Rismanchi, B. (2012). A review on cool thermal storage technologies and operating strategies. Renewable and sustainable energy reviews Arteconi, A., Hewitt, N. J., & Polonara, F. (2013). Domestic demand-side management (DSM): Role of heat pumps and thermal energy storage (TES) systems. Applied ThermalEngineering,51,155–165. Bayod-Rujula, A. A. (2009). Future development of the electricity systems with distributed generation. Energy, 34, 377–383. Hoving, J.H.K.(2014), Optimizing the performance of the ates coupled hvac system used at the kropman utrecht office (Master Thesis) ‘Load’, Available at: 1/ ‘Mass storage of energy panorama, 2013’, Available at: notes-de-synthese-panorama/panorama-2013 ‘SCADA System’, Available at:

M. Vahedipour Dahraie H. R. Najafi R. Nasirzadeh Azizkandi M.R. Nezamdoust (2012). Study on Compressed Air Energy Storage Coupled With a Wind Farm, Second Iranian Conference on Renewable Energy and Distributed Generation Steffen, B. (2012). Prospects for pumped-hydro storage in Germany. Energy Policy, 45, 420–429. Diaz-Gonzalez, F., Sumper, A., Gomis-Bellmunt, O., & Villafafila-Robles, R. (2012). A review of energy storage technologies for wind power applications. Renewable and Sustainable Energy Reviews. Kranz, S., & Frick, S. (2013). Efficient cooling energy supply with aquifer thermal energy y storages. Applied Energy, 109, 321–327. Miyata M., Yoshida H., Aono, M., Takegawa, T., Nagura, Y., Kobayashi, Y., Kim, J. (2007). Optimal

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